A behavior based locomotion controller with learning for disturbance compensation in bipedal robots

A novel behavior based locomotion controller (BBLC) capable of adapting to unknown disturbances is presented. The proposed controller implements a behavior based control architecture by subdividing the walking control into several task-space controllers such as swing leg control and center of gravity (COG) position control. For each task-space controller, a number of behaviors, which plan the reference task-space trajectories, are designed based on existing stabilizing controllers or strategies inspired by human walking biomechanics. A Q-learning algorithm is used to classify which behavior combinations can compensate for specific disturbances. The controller is implemented on a planar biped simulation with push type disturbances applied on flat and sloped terrain. The results show that stabilization strategies, capable of compensating for these disturbances emerge from the combination of different task level behaviors, without a priori knowledge of the nature of the disturbances.